Trace and observe agent workflows
See execution paths across prompts, models, tools, retrieval steps, latency, token usage, and outputs so you can follow a multi-step AI run from start to finish.
AI Observability Platform
Progress Telerik’s AI Observability Platform helps teams trace, debug, analyze cost, and evaluate AI agent workflows across production runs. It supports .NET, Python, and JavaScript and is positioned for developers, engineering leaders, and enterprise teams.
Overview
Progress Telerik’s AI Observability Platform is a developer-focused product for tracing, debugging, cost analysis, and evaluation of AI agent workflows. The site positions it around production AI systems such as agents, LLM apps, RAG systems, and copilots, where a single response may pass through prompts, retrieval, tools, retries, model calls, and custom workflow logic.
The platform is designed to give teams a full execution path and the operational context around each run: what happened, where it broke down, what it cost, and how good the output was. The homepage emphasizes live production traces, trace-level root cause analysis, cost and token visibility, and repeatable LLM evaluations tied to real execution data.
Core capabilities
Debug failures with AI-specific context
Use trace-level context to identify skipped tools, retrieval issues, bad context, loops, retries, errors, and workflow status changes that caused a failure.
Analyze AI cost and token usage
Track estimated cost and token usage across agents, workflows, models, providers, and workflow patterns to understand what drives spend.
Evaluate output quality over time
Run LLM-as-a-judge evaluations on captured traces and compare prompt, model, or workflow changes using real execution data.
Start from live agent runs
Install the SDK and instrument .NET, Python, or JavaScript apps to start capturing live traces in a dashboard.
Multiple views for the same workflow data
Review supporting views such as Trace Explorer, Workflow Debugging, Cost Attribution, and Quality Scorecards to inspect the same execution data from different angles.
Common workflows
Debug failing AI agent runs
Inspect prompts, retrieval steps, tool calls, retries, and outputs to understand where an agent run went off course and what changed in the execution path.
Troubleshoot MCP and multi-agent workflows
Analyze multi-step or multi-agent flows when work stalls, loops, or produces incomplete results, using trace context instead of piecing together separate logs.
Investigate retrieval and grounding issues
Review retrieved sources, prompt content, model responses, and final answers to identify why a RAG app produced a hallucination or pulled the wrong context.
Control token usage and cost
Track token usage and estimated cost across models, providers, agents, and workflows to find the steps or patterns that are driving AI spend.
Measure output quality and changes
Score trace outputs with LLM-as-a-judge evaluations and compare prompt, model, or workflow changes side by side using real runs.
Pros and Cons
Pros
- Covers the full AI workflow lifecycle from tracing and debugging to cost analysis and quality evaluation.
- Supports .NET, Python, and JavaScript according to the homepage and setup snippets.
- Focuses on real production traces rather than isolated logs, which helps explain failures in context.
- Provides concrete views for workflow debugging, cost attribution, and quality scorecards.
- Includes a free starting point with no credit card required and a short setup path according to the homepage.
Cons
- The source does not provide a complete integration list or documentation for every supported framework, provider, or external system.
- Pricing details are only partially shown in the collected text, so the full plan structure for this specific product is not fully visible in the source.
FAQ
Who is the AI Observability Platform for?
It is built for teams creating AI agents, LLM apps, RAG systems, and copilots that need trace-level visibility into how workflows run, where they fail, and what they cost. The site also highlights developer, engineering leader, and enterprise team use cases.
What does the platform help teams do?
The product focuses on tracing AI workflows, debugging failures, tracking token usage and estimated cost, and running LLM-as-a-judge evaluations on captured traces. The site also references data export, prompt management, model optimization, performance metrics, and an AI playground as related capabilities.
How do you get started?
The homepage says you can start free with no credit card and a 5-minute setup. It also shows SDK examples for .NET, Python, and JavaScript, with installation and instrumentation steps.
What integrations or platforms are supported?
The source does not list every integration in detail, but it explicitly shows SDK support for .NET, Python, and JavaScript and says the platform includes integrations with popular AI frameworks and model providers. The pricing page also mentions enterprise features such as SSO and SCIM in other Telerik products, but those are not confirmed for this AI Observability product.
Is it meant for production use?
The product is positioned around production traces, live agent runs, and captured execution data. The source does not describe offline-only analysis or unsupported environments in detail.
Quick Facts
- Category
- AI observability
- Primary users
- Developers, engineering leaders, and enterprise teams
- Supported languages
- .NET, Python, JavaScript
- Core workflows
- Tracing, debugging, cost analysis, LLM evaluations
- Source domain
- telerik.com
- Getting started
- Start free; no credit card required; 5-minute setup mentioned
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